. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022 Jan;54(1):4-17. Epub 2022 Jan 6 PubMed.


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  1. Microglia are central players in multiple brain diseases. While mouse datasets have been instrumental in deepening our understanding of the role of microglia in disease onset and progression, human transcriptomic datasets are needed to fully understand how microglia change across sex, age, brain regions, and in disease. In this fantastic study, de Paiva Lopes, Snijders, Humphrey et al. provide an important resource by generating a deep transcriptomic dataset of human microglia that includes both sexes, a wide age span, and four different brain regions dubbed the Microglial Genomic Atlas (MiGA).

    While human microglia transcriptomic datasets have become more prevalent in recent years, the number of patients profiled is often low. Furthermore, as we and others have shown, microglia are extremely sensitive to changes in their environment and experimental handling, which can lead to difficulties in comparing across datasets (Bohlen et al., 2017; Gosselin et al., 2017; Marsh et al., 2020). As this study demonstrates, there is a great need for large datasets, such as MiGA, to help overcome the inherent variability in human gene-expression profiles simply between individuals, but also as result of highly variable factors such as cause of death.

    The MiGA project uses bulk RNA sequencing to deeply characterize microglial transcriptomes, including splicing and isoform usage that have been challenging to generate in isolated microglia. Many previous studies simply used monocytes or other myeloid cells as proxies for microglia given this challenge. However, the MiGA paper clearly demonstrates that a high-quality dataset (especially in combination with other new datasets, i.e., Nott et al., 2019) can enable much stronger interpretation of GWAS loci in microglia, which can more precisely identify the risk gene of interest and the microglial-specific contribution to disease risk (Huang et al., 2017; Nott et al., 2019). Overall, this level of information will identify new potential pathways regulating microglia states and microglial contribution to disease risk in many different neurological diseases.

    MiGA highlights the importance of some of the key AD risk genes, but the study does have limitations. The loss of granularity due to the bulk sequencing method is probably underestimating the impact of many biological factors, including the impact of disease. It will be important in future studies to expand upon the work of MiGA using single-cell techniques. As recent studies have established, microglial responses to disease, injury, and other factors induce further changes in gene expression patterns, which may also be subject to technical pre-/postmortem variables (Keren-Shaul et al., 2017; Hammond et al., 2019; Marsh et al., 2020; Kamath et al., 2021). Therefore, it will be critical for similarly large-scale experiments, such as MiGA, to use these techniques to enable deeper characterization of eQTLs and disease risk across different subtypes of microglia across different contexts.

    Also, the use of individuals of European descent only for eQTL studies adds bias, reducing applicability of the findings to genetically diverse populations. Further sequencing and analysis of a more diverse population will be needed to make more generalizable interpretations of the impact of various AD risk genes and microglial expression patterns. These large-scale experiments will enable deeper characterization of eQTLs and disease risk across different states of microglia, a critical endeavor in our quest to understand the impact of microglia in disease contexts and opening new therapeutic avenues for many neurological diseases.


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    View all comments by Martine Therrien
  2. The authors report that microglial transcriptional heterogeneity, a measure of how well one has captured various microglial states, is quite poor across multiple brain regions, and between males and females, with the only biological factor strongly modulating microglial gene expression being age. Their data strongly supports that lipid homeostasis is among the genetic pathways that were most impacted in microglia by aging.

    Understanding how age-related deficits in lipid metabolism shape the microglial inflammatory response will be critical to fully elucidate the pathobiology of Alzheimer’s disease. Genetic studies like this one, in combination with empirical validation in models that can capture the complexity of human genetics, such as cells derived from human induced pluripotent stem cells, will give us a handle on identifying novel therapies to halt disease progression. Moreover, by integrating the heterogeneity that can be achieved through single-cell approaches with large genomics and transcriptomics studies, such as this one, we can build a better roadmap of the pathogenesis, which is urgently needed to develop effective interventions.

    View all comments by Li-Huei Tsai

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  1. Mighty MiGA: Microglial Genomic Atlas Zeros in on Causal AD Risk Variants